import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



class 							a				(     UpperCAmelCase__			,     unittest.TestCase			):







 """simple docstring"""






 lowerCamelCase      :Optional[int]            =						CanineTokenizer
 lowerCamelCase      :Tuple            =						False



 def 	UpperCAmelCase				(   self				)    ->   int:
        super().setUp()
        _A	  =    CanineTokenizer()
        tokenizer.save_pretrained(self.tmpdirname				)



 @cached_property
 def 	UpperCAmelCase				(   self				)    ->   Union[str, Any]:
        return CanineTokenizer.from_pretrained("""google/canine-s"""				)



 def 	UpperCAmelCase				(   self						,							**lowerCAmelCase_				)    ->   List[Any]:
        _A	  =    self.tokenizer_class.from_pretrained(self.tmpdirname						,							**_a				)
        _A	  =    10_24
        return tokenizer



 @require_torch
 def 	UpperCAmelCase				(   self				)    ->   Optional[Any]:
        _A	  =    self.canine_tokenizer
        _A	  =    ["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
        # fmt: off
        _A	  =    [5_73_44, 76, 1_05, 1_02, 1_01, 32, 1_05, 1_15, 32, 1_08, 1_05, 1_07, 1_01, 32, 97, 32, 98, 1_11, 1_20, 32, 1_11, 1_02, 32, 99, 1_04, 1_11, 99, 1_11, 1_08, 97, 1_16, 1_01, 1_15, 46, 5_73_45, 0, 0, 0, 0]
        # fmt: on
        _A	  =    tokenizer(_a						,							padding=_a						,							return_tensors="""pt"""				)
        self.assertIsInstance(_a						,							_a				)

        _A	  =    list(batch.input_ids.numpy()[0]				)

        self.assertListEqual(_a						,							_a				)

        self.assertEqual((2, 39)						,							batch.input_ids.shape				)
        self.assertEqual((2, 39)						,							batch.attention_mask.shape				)



 @require_torch
 def 	UpperCAmelCase				(   self				)    ->   Optional[int]:
        _A	  =    self.canine_tokenizer
        _A	  =    ["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
        _A	  =    tokenizer(_a						,							padding=_a						,							return_tensors="""pt"""				)
        # check if input_ids, attention_mask and token_type_ids are returned
        self.assertIn("""input_ids"""						,							_a				)
        self.assertIn("""attention_mask"""						,							_a				)
        self.assertIn("""token_type_ids"""						,							_a				)



 @require_torch
 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        _A	  =    self.canine_tokenizer
        _A	  =    [
            """What's the weater?""",
            """It's about 25 degrees.""",
        ]
        _A	  =    tokenizer(
            text_target=_a						,							max_length=32						,							padding="""max_length"""						,							truncation=_a						,							return_tensors="""pt"""				)
        self.assertEqual(32						,							targets["""input_ids"""].shape[1]				)



 def 	UpperCAmelCase				(   self				)    ->   int:
        _A	  =    self.get_tokenizers()
        for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
                      self.assertNotEqual(tokenizer.model_max_length						,							42				)

        # Now let's start the test
        _A	  =    self.get_tokenizers()
        for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
                      # Isolate this from the other tests because we save additional tokens/etc
                      _A	  =    tempfile.mkdtemp()

                      _A	  =    """ He is very happy, UNwant\u00E9d,running"""
                      _A	  =    tokenizer.encode(_a						,							add_special_tokens=_a				)
                      tokenizer.save_pretrained(_a				)

                      _A	  =    tokenizer.__class__.from_pretrained(_a				)
                      _A	  =    after_tokenizer.encode(_a						,							add_special_tokens=_a				)
                      self.assertListEqual(_a						,							_a				)

                      shutil.rmtree(_a				)

        _A	  =    self.get_tokenizers(model_max_length=42				)
        for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
                      # Isolate this from the other tests because we save additional tokens/etc
                      _A	  =    tempfile.mkdtemp()

                      _A	  =    """ He is very happy, UNwant\u00E9d,running"""

                      _A	  =    tokenizer.additional_special_tokens

                      # We can add a new special token for Canine as follows:
                      _A	  =    chr(0xe_007				)
                      additional_special_tokens.append(_a				)
                      tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}				)
                      _A	  =    tokenizer.encode(_a						,							add_special_tokens=_a				)
                      tokenizer.save_pretrained(_a				)

                      _A	  =    tokenizer.__class__.from_pretrained(_a				)
                      _A	  =    after_tokenizer.encode(_a						,							add_special_tokens=_a				)
                      self.assertListEqual(_a						,							_a				)
                      self.assertIn(_a						,							after_tokenizer.additional_special_tokens				)
                      self.assertEqual(after_tokenizer.model_max_length						,							42				)

                      _A	  =    tokenizer.__class__.from_pretrained(_a						,							model_max_length=43				)
                      self.assertEqual(tokenizer.model_max_length						,							43				)

                      shutil.rmtree(_a				)



 def 	UpperCAmelCase				(   self				)    ->   Union[str, Any]:
        _A	  =    self.get_tokenizers(do_lower_case=_a				)
        for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
                      _A  ,    _A	  =    self.get_clean_sequence(_a				)

                      # a special token for Canine can be defined as follows:
                      _A	  =    0xe_005
                      _A	  =    chr(_a				)

                      tokenizer.add_special_tokens({"""cls_token""": special_token}				)
                      _A	  =    tokenizer.encode(_a						,							add_special_tokens=_a				)
                      self.assertEqual(len(_a				)						,							1				)

                      _A	  =    tokenizer.decode(ids + encoded_special_token						,							clean_up_tokenization_spaces=_a				)
                      _A	  =    tokenizer.encode(_a						,							add_special_tokens=_a				)

                      _A	  =    tokenizer.encode(_a						,							add_special_tokens=_a				)
                      _A	  =    tokenizer.encode(_a						,							add_special_tokens=_a				)
                      self.assertEqual(_a						,							input_encoded + special_token_id				)

                      _A	  =    tokenizer.decode(_a						,							skip_special_tokens=_a				)
                      self.assertTrue(special_token not in decoded				)



 def 	UpperCAmelCase				(   self				)    ->   Union[str, Any]:
        _A	  =    self.get_tokenizers(do_lower_case=_a				)
        for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
                      _A	  =    chr(0xe_005				)
                      _A	  =    chr(0xe_006				)

                      # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                      tokenizer.add_tokens([SPECIAL_TOKEN_1]						,							special_tokens=_a				)
                      # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                      # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                      tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]}				)

                      _A	  =    tokenizer.tokenize(_a				)
                      _A	  =    tokenizer.tokenize(_a				)

                      self.assertEqual(len(_a				)						,							1				)
                      self.assertEqual(len(_a				)						,							1				)
                      self.assertEqual(token_a[0]						,							_a				)
                      self.assertEqual(token_a[0]						,							_a				)



 @require_tokenizers
 def 	UpperCAmelCase				(   self				)    ->   Optional[Any]:
        _A	  =    self.get_tokenizers(do_lower_case=_a				)
        for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
                      # a special token for Canine can be defined as follows:
                      _A	  =    0xe_006
                      _A	  =    chr(_a				)

                      _A	  =    AddedToken(_a						,							lstrip=_a				)
                      tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]}				)

                      with tempfile.TemporaryDirectory() as tmp_dir_name:
                             tokenizer.save_pretrained(_a				)
                             tokenizer.from_pretrained(_a				)



 def 	UpperCAmelCase				(   self				)    ->   Optional[Any]:
        _A	  =    []
        if self.test_slow_tokenizer:
               tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())				)

        if self.test_rust_tokenizer:
               tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())				)

        for tokenizer_class, tokenizer_utils in tokenizer_list:
               with tempfile.TemporaryDirectory() as tmp_dir:
                      tokenizer_utils.save_pretrained(_a				)

                      with open(os.path.join(_a						,							"""special_tokens_map.json"""				)						,							encoding="""utf-8"""				) as json_file:
                             _A	  =    json.load(_a				)

                      with open(os.path.join(_a						,							"""tokenizer_config.json"""				)						,							encoding="""utf-8"""				) as json_file:
                             _A	  =    json.load(_a				)

                      # a special token for Canine can be defined as follows:
                      _A	  =    0xe_006
                      _A	  =    chr(_a				)

                      _A	  =    [new_token_a]
                      _A	  =    [new_token_a]

                      with open(os.path.join(_a						,							"""special_tokens_map.json"""				)						,							"""w"""						,							encoding="""utf-8"""				) as outfile:
                             json.dump(_a						,							_a				)
                      with open(os.path.join(_a						,							"""tokenizer_config.json"""				)						,							"""w"""						,							encoding="""utf-8"""				) as outfile:
                             json.dump(_a						,							_a				)

                      # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                      # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                      # "special_tokens_map.json" files
                      _A	  =    tokenizer_class.from_pretrained(_a						,							extra_ids=0				)
                      self.assertIn(_a						,							tokenizer_without_change_in_init.additional_special_tokens				)
                      # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                      self.assertEqual(
                          [new_token_a]						,							tokenizer_without_change_in_init.convert_ids_to_tokens(
                              tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]				)				)						,							)

                      _A	  =    0xe_007
                      _A	  =    chr(_a				)
                      # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                      _A	  =    [AddedToken(_a						,							lstrip=_a				)]
                      _A	  =    tokenizer_class.from_pretrained(
                          _a						,							additional_special_tokens=_a						,							extra_ids=0				)

                      self.assertIn(_a						,							tokenizer.additional_special_tokens				)
                      # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                      self.assertEqual(
                          [new_token_a]						,							tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]				)				)				)



 @require_tokenizers
 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        _A	  =    self.get_tokenizers(do_lower_case=_a				)
        for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
                      _A	  =    """hello world"""
                      if self.space_between_special_tokens:
                             _A	  =    """[CLS] hello world [SEP]"""
                      else:
                             _A	  =    input
                      _A	  =    tokenizer.encode(_a						,							add_special_tokens=_a				)
                      _A	  =    tokenizer.decode(_a						,							spaces_between_special_tokens=self.space_between_special_tokens				)
                      self.assertIn(_a						,							[output, output.lower()]				)



 def 	UpperCAmelCase				(   self				)    ->   str:
        _A	  =    self.get_tokenizers()
        for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
                      _A	  =    [
                          """bos_token""",
                          """eos_token""",
                          """unk_token""",
                          """sep_token""",
                          """pad_token""",
                          """cls_token""",
                          """mask_token""",
                      ]

        _A	  =    """a"""
        _A	  =    ord(_a				)

        for attr in attributes_list:
               setattr(_a						,							attr + """_id"""						,							_a				)
               self.assertEqual(getattr(_a						,							_a				)						,							_a				)
               self.assertEqual(getattr(_a						,							attr + """_id"""				)						,							_a				)

               setattr(_a						,							attr + """_id"""						,							_a				)
               self.assertEqual(getattr(_a						,							_a				)						,							_a				)
               self.assertEqual(getattr(_a						,							attr + """_id"""				)						,							_a				)

        setattr(_a						,							"""additional_special_tokens_ids"""						,							[]				)
        self.assertListEqual(getattr(_a						,							"""additional_special_tokens"""				)						,							[]				)
        self.assertListEqual(getattr(_a						,							"""additional_special_tokens_ids"""				)						,							[]				)

        _A	  =    0xe_006
        _A	  =    chr(_a				)
        setattr(_a						,							"""additional_special_tokens_ids"""						,							[additional_special_token_id]				)
        self.assertListEqual(getattr(_a						,							"""additional_special_tokens"""				)						,							[additional_special_token]				)
        self.assertListEqual(getattr(_a						,							"""additional_special_tokens_ids"""				)						,							[additional_special_token_id]				)



 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        pass



 def 	UpperCAmelCase				(   self				)    ->   Any:
        pass



 def 	UpperCAmelCase				(   self				)    ->   Dict:
        pass



 def 	UpperCAmelCase				(   self				)    ->   str:
        pass



 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        pass



 def 	UpperCAmelCase				(   self				)    ->   Dict:
        pass



 def 	UpperCAmelCase				(   self				)    ->   Dict:
        pass



 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        pass










"""simple docstring"""




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowerCAmelCase				:     List[str]					=				object()

# For specifying empty leaf dict `{}`
lowerCAmelCase				:     Any					=				object()



def        a__					(							snake_case__			,					snake_case__							)		->   List[Any]:
			lowerCamelCase      =   tuple((re.compile(x + """$"""							) for x in qs)							)
			for i in range(len(snake_case__							) - len(snake_case__							) + 1							):
						lowerCamelCase      =   [x.match(snake_case__							) for x, y in zip(snake_case__			,					ks[i:]							)]
						if matches and all(snake_case__							):
									return True
			return False



def        a__					(							snake_case__							)		->   str:
			def replace(snake_case__			,					snake_case__							):
						for rule, replacement in rules:
									if _match(snake_case__			,					snake_case__							):
												return replacement
						return val

			return replace



def        a__					(							)		->   Union[str, Any]:
			return [
			    # embeddings
			    (("transformer", "wpe", "embedding"), P("""mp"""			,					snake_case__							)),
			    (("transformer", "wte", "embedding"), P("""mp"""			,					snake_case__							)),
			    # atention
			    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(snake_case__			,					"""mp"""							)),
			    (("attention", "out_proj", "kernel"), P("""mp"""			,					snake_case__							)),
			    (("attention", "out_proj", "bias"), None),
			    # mlp
			    (("mlp", "c_fc", "kernel"), P(snake_case__			,					"""mp"""							)),
			    (("mlp", "c_fc", "bias"), P("""mp"""							)),
			    (("mlp", "c_proj", "kernel"), P("""mp"""			,					snake_case__							)),
			    (("mlp", "c_proj", "bias"), None),
			    # layer norms
			    ((r"ln_\d+", "bias"), None),
			    ((r"\d+", r"ln_\d+", "scale"), None),
			    (("ln_f", "bias"), None),
			    (("ln_f", "scale"), None),
			]



def        a__					(							snake_case__							)		->   Optional[Any]:
			lowerCamelCase      =   _get_partition_rules()
			lowerCamelCase      =   _replacement_rules(snake_case__							)
			lowerCamelCase      =   {k: _unmatched for k in flatten_dict(snake_case__							)}
			lowerCamelCase      =   {k: replace(snake_case__			,					snake_case__							) for k, v in initd.items()}
			assert _unmatched not in result.values(), "Incomplete partition spec."
			return freeze(unflatten_dict(snake_case__							)							)



